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Nonparametric Multivariate Density Estimation: Case Study of Cauchy Mixture Model

Mathematics, 2021
Estimation of probability density functions (pdf) is considered an essential part of statistical modelling. Heteroskedasticity and outliers are the problems that make data analysis harder. The Cauchy mixture model helps us to cover both of them.
Tomas Ruzgas, Mantas Lukauskas, Gedmantas Čepkauskas   +2 more
doaj   +2 more sources

Learning Continuous Decomposable Models Using Mutual Information and Statistical Copulas [PDF]

Entropy
Learning dependence graphs from multivariate continuous data is challenging when marginal distributions are heterogeneous, since likelihood-based nonparametric scores can be sensitive to smoothing choices and can confound marginal irregularities ...
Luiz Desuó Neto, Henrique de Oliveira Caetano, Matheus de Souza Sant’Anna Fogliatto, Carlos Dias Maciel   +3 more
doaj   +2 more sources

Quasar Identification Using Multivariate Probability Density Estimated from Nonparametric Conditional Probabilities

Mathematics, 2022
Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE).
Jenny Farmer, Eve Allen, Donald J. Jacobs   +2 more
doaj   +2 more sources

Nonparametric density estimation for multivariate bounded data [PDF]

Journal of Statistical Planning and Inference, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Taoufik Bouezmarni, Jeroen V.K. Rombouts
openaire   +5 more sources

Optimal Bayesian Estimation of a Regression Curve, a Conditional Density, and a Conditional Distribution

Mathematics, 2022
In this paper, several related estimation problems are addressed from a Bayesian point of view, and optimal estimators are obtained for each of them when some natural loss functions are considered.
Agustín G. Nogales
doaj   +1 more source

BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures

Journal of Statistical Software, 2021
BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust generalization of the popular class of Dirichlet process mixture models.
Riccardo Corradin, Antonio Canale, Bernardo Nipoti   +2 more
doaj   +1 more source

Nonparametric density estimation using a multidimensional mixture model of Gaussian distributions

Lietuvos Matematikos Rinkinys, 2005
This paper algorithmically and empirically studies five major types of nonparametric multivariate density estimation techniques, where no assumption is made about data being drawn from any of known parametric families of distribution. There is developed
Tomas Ruzgas, Mindaugas Kavaliauskas
doaj   +3 more sources

Nonparametric estimation of multivariate elliptic densities via finite mixture sieves [PDF]

Journal of Multivariate Analysis, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Battey, HS, Linton, O
openaire   +8 more sources

A Nonparametric Estimate of a Multivariate Density Function

The Annals of Mathematical Statistics, 1965
Let $x_1, \cdots, x_n$ be independent observations on a $p$-dimensional random variable $X = (X_1, \cdots, X_p)$ with absolutely continuous distribution function $F(x_1, \cdots, x_p)$. An observation $x_i$ on $X$ is $x_i = (x_{1i}, \cdots, x_{pi})$. The problem considered here is the estimation of the probability density function $f(x_1, \cdots, x_p ...
Loftsgaarden, D. O., Quesenberry, C. P.
openaire   +3 more sources

Probability density estimation using data projection

Lietuvos Matematikos Rinkinys, 2009
Nonparametric estimation of multivariate multimodal probability density is analysed. The projection pursuit density estimator was proposed by J.H. Friedman.
Mindaugas Kavaliauskas
doaj   +1 more source